{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c001744",
   "metadata": {},
   "source": [
    "## 任务1 计算 平均缴费金额、平均缴费次数，并以 csv 格式输出结果保存为                                           \n",
    "   ## “居民客户的用电缴费习惯分析 1.csv”"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93bed60",
   "metadata": {},
   "source": [
    "### 1.1 导入相关python库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c074fefb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45fa9d57",
   "metadata": {},
   "source": [
    "### 1.2. 探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486c0f70",
   "metadata": {},
   "source": [
    "数据来源于中国软件杯官网A5-电力客户行为分析 http://www.cnsoftbei.com/plus/view.php?aid=715                                   \n",
    "测试数据或平台部分所提供的测试数据：cph.xlsx\n",
    "将其改名为电力1.xlsx后放入jupyter进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "224953c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     用户编号   缴费日期  缴费金额（元）\n",
      "0  1000000001 2018-03-01             101\n",
      "1  1000000001 2018-05-03              80\n",
      "2  1000000001 2018-07-02             150\n",
      "3  1000000001 2018-08-02             200\n",
      "4  1000000001 2018-09-05             220\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.unicode.east_asian_width',True)\n",
    "df = pd.read_excel('电力1.xlsx')     # 读取excel文件\n",
    "print(df.head()) #输出数据的前5行"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4056e079",
   "metadata": {},
   "source": [
    "### 1.3 分析并将输出结果保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7aa9368",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       user_id  平均缴费次数 平均缴费金额\n",
      "0   1000000001             8      123.375\n",
      "1   1000000002             7       70.000\n",
      "2   1000000003             7      168.571\n",
      "3   1000000004             8       77.625\n",
      "4   1000000005             7      214.286\n",
      "..         ...           ...          ...\n",
      "95  1000000096             7      130.857\n",
      "96  1000000097             6      108.167\n",
      "97  1000000098             6      122.500\n",
      "98  1000000099             6      142.167\n",
      "99  1000000100             7      134.714\n",
      "\n",
      "[100 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "totall=0\n",
    "time=0\n",
    "user=[]\n",
    "for i in range(0,664):\n",
    "    if i!=663 and df.iloc[i, 0]==df.iloc[i+1,0]:\n",
    "        time+=1\n",
    "        totall+=df.iloc[i,2]\n",
    "    else:\n",
    "        time+=1\n",
    "        totall+=df.iloc[i,2]\n",
    "        user1=[]\n",
    "        user1.append(df.iloc[i, 0])\n",
    "        user1.append(time)\n",
    "        user1.append('{:.3f}'.format(totall/time))\n",
    "        time=0\n",
    "        totall=0\n",
    "        user.append(user1)\n",
    "names = [\"user_id\",\"平均缴费次数\",\"平均缴费金额\"]\n",
    "test = pd.DataFrame(columns = names,data=user)\n",
    "test.to_csv('居民客户的用电缴费习惯分析 1.csv',index=False,encoding='utf_8_sig')\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee994f59",
   "metadata": {},
   "source": [
    "### 1.4 对平均缴费次数\",\"平均缴费金额\"相关可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5876a0b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('居民客户的用电缴费习惯分析 1.csv')     # 读取excel文件"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34620c80",
   "metadata": {},
   "source": [
    "#### '平均缴费次数'的箱线图和带有正态拟合的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0006071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "x=data['平均缴费次数']\n",
    "plt.boxplot(x)\n",
    "plt.grid(axis='y',ls=':',lw=1,color='gray',alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd156d95",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\acon\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.平均缴费次数,kde=True, fit=stats.norm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59211989",
   "metadata": {},
   "source": [
    "从以上图可见100个用户的平均缴费次数大体集中在6次和7次"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80e7beb7",
   "metadata": {},
   "source": [
    "#### '平均缴费金额'的箱线图和带有正态拟合的直方图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48434734",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "x=data['平均缴费金额']\n",
    "plt.boxplot(x)\n",
    "plt.grid(axis='y',ls=':',lw=1,color='gray',alpha=0.4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d8f5f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\acon\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.平均缴费金额,kde=True, fit=stats.norm)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c4b438",
   "metadata": {},
   "source": [
    "从以上图可见100个用户的平均缴费金额大体分布于70~150的区间上"
   ]
  },
  {
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